Course Details

ELE 694 Biomedical Signal Processing
2021-2022 Fall term information

The course is not open this term

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University ( in real-time and displayed here. Please check the appropriate page on the original site against any technical problems. Course data last updated on 24/10/2021.


Course Name Code Semester Theory
Credit ECTS
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Drill and Practice
Case Study
Problem Solving
Instructor (s)Department Faculty 
Course objectiveThe course objective is to understand the basics of signal processing theory and utilizing some useful signal processing tools and methods efficiently for the signals frequently encountered in the fields of biology and medicine. 
Learning outcomes
  1. A student completing the course successfully will know the basics of signal processing theory to be used in biomedical studies,
  2. Learn the classification of biomedical signals and system modelling approaches,
  3. Have basic signal processing tools for the practical biomedical field problems,
  4. Efficiently use relevant computer programming tools for developing problem solutions,
  5. Learn possible use of artificial intelligence techniques in signal processing and biomedical applications,
Course Content1. Introduction to biomedical signal processing
2. Classification of biomedical signals
3. Signals and measurements of biological systems: ECG,EEG,EMG
4. Memory and correlation analysis
5. Continuous and discrete models
6. Noise sources in biomedical systems
7. Noise cancellation and signal conditioning
8. Spectral analysis and modeling
9. Feature extraction, classification and artificial intelligence
ReferencesLecture Notes.

Bruce, E.N., Biomedical Signal Processing and Signal Modeling, John Wiley &
Sons, 2001.

Rangayyan, R. M., Biomedical Signal Analysis: A case-study approach, IEEE
Press/Wiley Inter-Science, 2002.

Oppenheim, A.V., Willsky, A.S., Signals and Systems, 2nd Edt, Prentice-Hall, 1997.

Course outline weekly

Week 1Introduction to biomedical signal processing
Week 2Classification of biomedical signals
Week 3Signals and measurements of biological systems: ECG,EEG
Week 4Signals and measurements of biological systems: EMG, EOG
Week 5Memory and correlation analysis
Week 6Continuous time signals and models
Week 7Discrete time signals and models
Week 8Midterm Exam I
Week 9Noise sources in biomedical systems
Week 10Noise cancellation and signal conditioning
Week 11Spectral analysis and modeling
Week 12Midterm Exam II
Week 13Feature extraction, classification
Week 14Artificial intelligence in biomedical applications
Week 15Final exam
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Field activities00
Specific practical training00
Final exam160
Percentage of semester activities contributing grade succes460
Percentage of final exam contributing grade succes140

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)13452
Presentation / Seminar Preparation000
Homework assignment22040
Midterms (Study duration)22040
Final Exam (Study duration) 13030
Total Workload3277204

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.   X 
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.    X
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.    X
4. Designs and runs research projects, analyzes and interprets the results.   X 
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.   X 
6. Produces novel solutions for problems.   X 
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.   X 
8. Follows technological developments, improves him/herself , easily adapts to new conditions.    X 
9. Is aware of ethical, social and environmental impacts of his/her work.  X  
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively  X  

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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